carscore estimates the vector of CAR scores, either using the standard
empirical estimator of the correlation matrix, or a shrinkage estimator.

Usage

1

Arguments

Xtrain

Matrix of predictors (columns correspond to variables).

Ytrain

Univariate response variable.

lambda

The correlation shrinkage intensity (range 0-1).
If not specified (the default) it is estimated using an
analytic formula from Sch\"afer and Strimmer (2005). For lambda=0
the empirical correlations are used.

diagonal

For diagonal=FALSE (the default) CAR scores are computed;
otherwise with diagonal=TRUE marginal correlations.

verbose

If verbose=TRUE then the shrinkage intensity
used in estimating the shrinkage correlation matrix is reported.

Details

The CAR scores are the correlations between the response and the
Mahalanobis-decorrelated predictors. CAR score is an abbreviation
for Correlation-Adjusted (marginal) coRelation, where the first
correlation matrix refers dependencies among predictors.

In Zuber and Strimmer (2011) it
is argued that squared CAR scores are a natural measure for variable
importance and it is shown that variable selection based on CAR scores
is highly efficient compared to competing approaches such as elastic net
lasso, or boosting.

If the response is binary (or descrete) the corresponding quantity
are CAT scores (see catscore).